Abstract: A remote vehicle control system includes a vehicle mounted sensor system including a video camera system for producing video data and a distance mapping sensor system for producing distance map data. A data handling system is used to compress and transmit both the video and distance map data over a cellular network using feed forward correction. A virtual control system acts to receive the video and distance map data, while providing a user with a live video stream supported by distance map data. Based on user actions, control instructions can be sent to the vehicle mounted sensor system and the remote vehicle over the cellular network.

Abstract: A remote vehicle control system includes a vehicle mounted sensor system including a video camera system for producing video data and a distance mapping sensor system for producing distance map data. A data handling system is used to compress and transmit both the video and distance map data over a cellular network using feed forward correction. A virtual control system acts to receive the video and distance map data, while providing a user with a live video stream supported by distance map data. Based on user actions, control instructions can be sent to the vehicle mounted sensor system and the remote vehicle over the cellular network.

Abstract: A remote vehicle control system includes a vehicle mounted sensor system including a video camera system for producing video data and a distance mapping sensor system for producing distance map data. A data handling system is used to compress and transmit both the video and distance map data over a cellular network using feed forward correction. A virtual control system acts to receive the video and distance map data, while providing a user with a live video stream supported by distance map data. Based on user actions, control instructions can be sent to the vehicle mounted sensor system and the remote vehicle over the cellular network.

Abstract: A remote vehicle control system includes a vehicle mounted sensor system including a video camera system for producing video data and a distance mapping sensor system for producing distance map data. A data handling system is used to compress and transmit both the video and distance map data over a cellular network using feed forward correction. A virtual control system acts to receive the video and distance map data, while providing a user with a live video stream supported by distance map data. Based on user actions, control instructions can be sent to the vehicle mounted sensor system and the remote vehicle over the cellular network.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an object classification method includes receiving sensor data associated with an object observed by a sensor system of an autonomous vehicle and determining, with a processor, a bounding curve associated with the sensor data. A plurality of bounding curve features are determined based on a set of convexities and concavities associated with the bounding curve. The object is classified by applying the plurality of bounding curve features to a machine learning model and receiving a classification output.

Abstract: Systems and method are provided for classifying an object. In one embodiment, a method includes receiving sensor data associated with an environment of a vehicle; processing, by a processor, the sensor data to determine an element within a scene; generating, by the processor, a bounding box around the element; projecting, by the processor, segments of the element onto the bounding box to obtain a depth image; and classifying the object by providing the depth image to a machine learning model and receiving a classification output that classifies the element as an object for assisting in control of the autonomous vehicle.

Abstract: Systems and method are provided for controlling a vehicle. In one embodiment, an object classification method includes receiving sensor data associated with an object observed by a sensor system of an autonomous vehicle and determining, with a processor, a bounding curve associated with the sensor data. A plurality of bounding curve features are determined based on a set of convexities and concavities associated with the bounding curve. The object is classified by applying the plurality of bounding curve features to a machine learning model and receiving a classification output.